IW:LEARN 7 Years of Plone

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IW:LEARN 7 Years of Plone

In 1997 the Global Environment Facility (GEF)initiated the International Waters: Learning Exchange and Resource Network (IW:LEARN) as a knowledge management project to enhance the delivery of results by GEF IW projects.

IW:LEARN 7 Years of Plone

IW:LEARN is a joint UNEP/UNDP project which works with GEF (Global Environment Facility) International Waters projects to improve online sharing of data and information relevant to managing international waters, including marine, coastal and freshwater ecosystems.

IW:LEARN 7 Years of Plone

The IW:LEARN Team is spread around the globe (Bratislava, Nairobi, Bangkok and formerly Washington DC).

IW:LEARN 7 Years of Plone

Website: http://iwlearn.net

Training

Hosting: http://project-name.iwlearn.org

Archive

IW:LEARN 7 Years of Plone

The website serves as: Information and News Hub

Document clearinghouse

Central repository for learning Materials

Portfolio visualization tool

IW:LEARN 7 Years of Plone

Short Term contract with the UN in NairobiDate: 2005-05-16 We are looking for Plone Developers to help us deploy the CMS for a number of distributed applications.We are seeking plone experts with demonstrated skills for short term consultancies (6-12 months) with possibilities of extension.

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

Cost (free to download and install)Avoids dependence on vendors (vendor lock-in) for support and servicesAvoid costly upgrades and dropping of support of older software versionsSecurity with many eyes all bugs are shallow

Controlover the use of one's ideas really constitutes control over other people's lives; and it is usually used to make their lives more difficult.

IW:LEARN 7 Years of Plone

Adoption of open standards (avoid non-standard formats and compatibility issues in proprietary software)Software can be readily customized (source code can be viewed and edited by anyone)Distribution to stakeholders is not restrictedAny translation possible investment may be too great for proprietary software developerFast development cycle, rapid response time to bug reports

IW:LEARN 7 Years of Plone

Engaging Stakeholders:

Events Calendar

News Federation (feedfeeder)

Newsletter (EasyNewsletter)

IW:LEARN 7 Years of Plone

Metadata

Everybody loves it but it is a challenge to get good metadata

Title

Description

Language

Related items

Keywords

IW:LEARN 7 Years of Plone

Description:collective.ots

Extracts a meaningful abstract out of long documents.The output is not perfect but a good starting point.

IW:LEARN 7 Years of Plone

Language

collective.langdet

Guess the language with ngram statistical methods

ar: 1, bg: 1, cy: 2, en: 24200, es: 278, et: 1, fr: 204, hr: 6, id: 52, it: 246, pt: 66, ro: 17, ru: 42, sk: 2, sq: 3, sr: 1, sv: 3, tl: 2,tn: 24, uk: 2, zh: 1

IW:LEARN 7 Years of Plone

Related Items

A powerful way to improve UX and Page RankHard to do manually

collective.simserver

IW:LEARN 7 Years of Plone

What is a document similarity service?

Conceptually, a service that lets you : train a semantic model from a corpus of plain texts (no manual annotation and mark-up needed)

index arbitrary documents using this semantic model

query the index for similar documents (the query can be either an uid of a document already in the index, or an arbitrary text)

IW:LEARN 7 Years of Plone

Simserver is built on Gensim. Gensim is a free Python framework designed to automatically extract semantic topics from documents, as efficiently (computer-wise) and painlessly (human-wise) as possible.

IW:LEARN 7 Years of Plone

Gensim aims at processing raw, unstructured digital texts (plain text). The algorithms in gensim, such as Latent Semantic Analysis, Latent Dirichlet Allocation or Random Projections, discover semantic structure of documents, by examining word statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised, which means no human input is necessary you only need a corpus of plain text documents.

IW:LEARN 7 Years of Plone

Demo

IW:LEARN 7 Years of Plone

Visualizations

Maps built on top of collective.geo

Charts with pygal (SVG)

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

Maps of:

EventsContact LocationsProject Basins (Choropleth)Project Countries (Choropleth)Project Management Units (Cluster)Project Details

IW:LEARN 7 Years of Plone

Dynamic Charts

Project ratingsRegionsAgencies

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

Apart from Plone:

KARL Knowledge Management System

Geonode share spatial data

IW:LEARN 7 Years of Plone

Thank You

Questions?

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

IW:LEARN 7 Years of Plone

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Google Page Rank

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